Marginal plots enrich a main plot (commonly a scatter plot) by adding additional visualizations along the margins of the axes.
These marginal plots allow you to highlight the distribution of data along a single axis, or to compare across both axes.
They are widely used in exploratory data analysis and visualization to combine context + relationship in a single figure.
Marginal plot layouts
The position of marginal plots defines the type of layout:
One-side → distribution along a single axis.
Two-sides → comparison along both axes.
Layers → multiple plot types stacked on the same axis.
Marginal plots inventory
Different plot types can serve as marginal visualizations.
Histograms, boxplots, density curves, strip plots, and violins are common options.
They can be combined in many ways depending on the analytic goal.
How to code marginal plots?
Below there are common recipes used in Python and R
Python (Seaborn, jointplot)
import seaborn as snsimport matplotlib.pyplot as plt# Datasettips = sns.load_dataset("tips")# Lines to generate the MAIN + MARGINAL PLOTSg = sns.jointplot( data=tips, x="total_bill", y="tip", kind="scatter", height=6, marginal_ticks=True, joint_kws={"s": 80, "alpha": 0.8})sns.set_theme(style="white")# Avoid this line_ = g.set_axis_labels("Total bill (USD)", "Tip (USD)", labelpad=10)# Optional: some cleaningfor ax in (g.ax_marg_x, g.ax_marg_y): _ = sns.despine(ax=ax, top=True, right=True, left=True, bottom=True) ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)plt.show()
R (ggplot2+ggExtra)
library(ggplot2)library(ggExtra)# regular ggplot codep2 <-ggplot(mtcars, aes(x = mpg, y = wt)) +geom_point(color ="darkgreen", size =4, alpha =0.6)+theme_classic()+labs(x ="Miles per gallon (mpg)",y ="Weight (1000 lbs)",title ="Fuel efficiency vs. vehicle weight",subtitle ="With marginal densities" )# Lines to add the MARGINAL PLOTSggMarginal(p2, type ="density", fill ="lightgreen", color ="darkgreen", alpha =0.5)
